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RTI-USU DiscussionVirtual, June 3, 2015

Science to support water resource operations and management

Andy Wood and Martyn ClarkNCAR Research Applications LaboratoryHydrometeorological Applications Program

Key reports

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User needs provide agency motivation

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Other Categories

Streamflow Prediction System Elements

Candidate opportunities for forecast improvement

1) alternative hydrologic model(s), 2) new forcing data/methods (eg, QC) to drive hydrologic modeling3) new calibration tools to support hydrologic model implementation 4) Improved data assimilation to specify initial watershed conditions for

hydrologic forecasts5) new data and methods to predict future weather and climate 6,7) methods to post-process streamflow forecasts and reduce systematic errors8) benchmarking / hindcastsing / verification system / ensembles (not shown)

Watershed Modeling Dataset

• Goal: framework for calibrating and running watershed models CONUS-wide– including for short range and seasonal ensemble forecasting

• Basin Selection– Used GAGES-II, Hydro-climatic data network (HCDN)-2009

• Initial Data & Models, Calibration Approach– Forcing via Daymet (http://daymet.ornl.gov/)– NWS operational Snow-17 and Sacramento-soil moisture accounting model (Snow-17/SAC)– Shuffled complex evolution (SCE) global optimization routine

Andy Newman

Hydrologic modelingRevealing impacts of model choice

Propositions:1.Most hydrologic modelers share a common

understanding of how the dominant fluxes of water and energy affect the time evolution of thermodynamic and hydrologic states

▫ The collective understanding of the connectivity of state variables and fluxes allows us to formulate general conservation equations in different sub-domains

▫ The conservation equations are scale-invariant

2.Differences among models relate toa) the spatial discretization of the model domain;b) the approaches used to parameterize

individual fluxes (including model parameter values); and

c) the methods used to solve the governing model equations.

General schematic of the terrestrial water cycle, showing dominant fluxes of water and energy

Given these propositions, it is possible to develop a unifying model framework

For example, by defining a single set of conservation equations, with the capability to use different spatial discretizations (e.g., multi-scale grids, HRUs; connected or disconnected), different flux parameterizations and model parameters, and different time stepping schemes

Clark et al. (WRR 2011); Clark et al. (WRR 2015a; 2015b)

soil soil

aquiferaquifer

soilsoil

aquifer

soil

c) Column organization

a) GRUs b) HRUs

i) lump ii) grid

iii) polygon

The unified approach to hydrologic modeling (SUMMA)

Governing equations

Hydrology

Thermodynamics

Physical processes

XXX Model options

Evapo-transpiration

Infiltration

Surface runoff

SolverCanopy storage

Aquifer storage

Snow temperature

Snow Unloading

Canopy interception

Canopy evaporation

Water table (TOPMODEL)Xinanjiang (VIC)

Rooting profile

Green-AmptDarcy

Frozen ground

Richards’Gravity drainage

Multi-domain

Boussinesq

Conceptual aquifer

Instant outflow

Gravity drainage

Capacity limited

Wetted area

Soil water characteristics

Explicit overland flow

Atmospheric stability

Canopy radiation

Net energy fluxes

Beer’s Law

2-stream vis+nir

2-stream broadband

Kinematic

Liquid drainage

Linear above threshold

Soil Stress function Ball-Berry

Snow drifting

LouisObukhov

Melt drip

Linear reservoir

Topographic drift factors

Blowing snowmodel

Snowstorage

Soil water content

Canopy temperature

Soil temperature

Phase change

Horizontal redistribution

Water flow through snow

Canopy turbulence

Supercooled liquid water

K-theory

L-theory

Vertical redistribution

Martyn Clark

Pragmatic Model Architectures & Physicswhat are appropriate/tractable scales & complexity to capture variability?

Snow17-Sacramento

SUMMA-Sacramento

One Lump HRUs Elevation Bands

Crystal River

Diagnosis of Model States – eg, SWE

June 20th, 1983

Near start of rise to peak

• Snow17-HRU too much in lower elevations

• Snow17-lump too little in higher elevations

• SUMMA-band too little in higher elevations

• Snow17-band probably about right given flow performance

SUMMA

High Flow Year -- 1983

Snow17-lumpSnow17-hruSnow17-bandSUMMA-band

June 20

Clark & Slater, 2006 – JHM

1) Estimate probability of precipitation (POP), amount and error at each grid cell

2) Synthesize ensemblesfrom POP, amount & error

station observations

____generatespatial

correlation structure & uncertainty

Example: Precip over the Colorado Headwaters

DA Datasets -- Creating Met. Forcing Uncertainty

Andy Newman, NCAR

The interpolation of station obs to gridded fields can generate many equally valid realizations (analyses)

Most existing datasets just provide a single realization.

Example CONUS Precipitation & Temperature• ~12,000 stations used for analysis• target is 1/8o grid (~12 km), all CONUS land pixels• 100-member forcing ensemble

Example CONUS Precipitation April 2008 example

Estimating this uncertainty is valuable for:• More robust model calibration• Input to data assimilation techniques, which require specification of model

uncertainty

• Example Application• Snowmelt dominated basin in Colorado Rockies• Example water year daily temperature (a)• Snow water equivalent accumulation (b)

• Simple temperature index model (optimized for Daymet (green))

Ensemble Hydrometorology Dataset

operational example of automated DA

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Alternatives to manual spinup: ensemble initializations (particle filter)

system by Amy Sansone, Matt Wiley, 3TIERslide from DOH Mtg talk, 2012

Example: Flood forecasting• Opportunity: downscaled ensemble met forecasts enable estimation of

prediction uncertainty• Benefits: supports risk-based approaches for forecast use• Specs: use locally-weighted multi-variate regression to downscale GEFS

(reforecast) atmospheric predictors to watershed precipitation and temperature

Figures: Case study hindcast of 15-day ensemble forecast including 7 days of downscaled GEFS as met forecast(Snow17/SAC model)

Real-time demonstration and evaluation• In case study basins, demonstrate and evaluate experimental, automated

days-to-seasons flow forecasts using:• met and flow data quality control and various real-time forcing generation

approaches• ensemble meteorological forecasts and downscaling techniques• variations in model physics and architecture• automated, objective model calibration• data assimilation• flow forecast post-processing• hindcasting and verification

• Partner with USACE/Recl.

field office personnel for

evaluation and to guide

product development

• Bart Nijssen, U. Washington,

is a collaborator

Initial case study set for real-time prediction demo

• chosen for varying hydroclimates, being relatively unimpaired, and feeding reservoir inflows -- subset of nation-wide model dataset

http://www.ral.ucar.edu/staff/wood/case_studies/

Thank You

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andywood@ucar.edu & mclark@ucar.edu

Harnessing Seasonal Climate Forecasts

• Opportunity: seasonal climate forecasts can add information to seasonal streamflow predictions

• Benefits? increased skill benefits water supply forecasts and associated applications

• Specs: use ensemble trace-weighting approaches based on likelihood from regression of predictors

e.g., climate system variables or climate forecasts

Hydrologic Hindcasting• Objectives:

• Evaluate alternative process variations• Specify hindcast experiments to address specific questions• Inform future real-time system design

• Forecast Types• Flood:

• 5-10 year hindcast• daily updating• leads 1-7 days

• Seasonal:• 30+ year hindcast• weekly updating• lead time 1 year